These CEP Guidelines provide clear policies and practical guidance for planning, approving, and managing experiential and community-engaged projects. The document defines roles and responsibilities for students, faculty, and external partners; outlines approval steps, milestone timelines, deliverables, and assessment criteria; and links to templates, forms, and support resources.
Use this guide to align project design with program learning outcomes, coordinate stakeholder expectations, and ensure equitable, consistent implementation and evaluation.
1.1 Expected Student Learning Outcomes
Be the end of the program, students should be able to:
SLO 1. Translate a marketing problem into research questions or hypotheses that will drive the research project.
SLO 2. Evaluate known principles of consumer psychology and behaviors appropriate to address the marketing problems.
SLO 3. Build a model appropriate for addressing the project objectives.
SLO 4a. Make data-informed recommendations on marketing strategy.
SLO 4b. Develop an effective digital marketing plan.
SLO 5a. Effectively communicate various components orally.
SLO 5b. Effectively communicate various components in writing.
1.2 Project Duration and Group Formation
All MSDM students will engage in a project as a group (4-6 students per group) advised by a faculty project lead (faculty lead hereafter). The group size will depend on the final number of students enrolled and faculty projects available.
The projects will be completed in three semesters and will be facilitated in IBM 6200 / IBM6500 in the fall, IBM 6800 in the spring, and IBM 6950 in the summer.
1.3 Timeline
For the timeline and milestone, please refer to ?@sec-project-milestones-and-timelines of the main Capstone project page.
1.4 Who Initiates the Project?
The projects will be developed in one of two ways:
OPTON 1: Early in the Fall semester, the MSDM director will send out a call for proposals from the faculty. The MSDM committee will review the submissions and forward selected proposals to the director. The director will then determine the final number of accepted proposals, taking into account the quality of the proposals, student demand, and budget. The IBM 6200 will host a faculty research forum featuring proposed faculty-led projects, where participants will present initial ideas for data analytics projects. Students will review the projects and submit their preferences, as a group, for the projects presented. Then, the MSDM program will assign projects to student groups, taking into account their preferences and individual skill sets. Students will work on the assigned project under the supervision of the faculty lead and the instructors for three terms.
OPTION 2: If students have an acceptable project idea (e.g., they have a client company and its data), they can submit proposal according to the call for proposal, but read ?@sec-call-for-project-proposals and consult with Dr. Jung, who will help you develop the idea furhter and help you identify expert to invite. If the director decides that the proposed project is suitable, he will work with students to identify suitable faculty members for the project. If a faculty advisor is identified and consents to supervise the project, then the student will be allowed to pursue it. To be considered for this option, students must present their idea to the Idirector by the end of the August 17, the first day of Fall semester. However to allow sufficient time to explore feasibility before applying, students are encouraged to plan ahead. Attend information session in early July (see ?@sec-informational-session).
1.5 Who has the main responsibility for leading the project?
If there is a disagreement between the faculty lead and the instructor(s) on the direction of the project, the faculty lead has the ultimate authority and power to determine what is appropriate for the project because the names and signatures of the faculty lead will be permanently recorded in the BroncoWorks, CPP’s library’s publication, searchable and viewable online, while instructors’ names, except for the instructor of IBM 6950, will not be part of the publication. However, the faculty lead should make reasonable accommodations for the recommendations made by the instructors as well as the audience who attended the presentations.
Although the faculty lead will initially propose the overall structure of the project, students will be in charge of developing the project. The intention of the program is that students experience all aspects of a data analytics project such as identifying issues, developing analytics objectives, selecting appropriate analytic methods, analyzing data, interpreting the results and providing useful recommendations.
1.6 Accountability
To ensure that each student adequately demonstrates the MSDM program’s student learning outcomes (e.g., analytical and organizational skills, critical thinking ability, and appropriate written and oral communication skills), the faculty lead must ensure that a sufficient and equitable portion of the project is delegated to each member and each student is assessed.
The class instructors will determine group scores in collaboration with the faculty lead (and clients), while the students may decide how the group score will be assigned for each group member via group member evaluations. If faculty observe significant differences in their efforts and performance, they may decide to assign individual scores to the group members, rather than a single group score.
1.7 Types of Projects
There are two different types of projects. Based on the nature of the project, students will follow the appropriate guidelines below.
1.7.1 (1) Client Consulting Project
There should be a company (existing or start-up) for which you will solve business problems through the project.
The goal is to solve a problem faced by the company, whether the method you use is new or not. Your goal is not to discover new knowledge.
To help the company and implement changes, you should have a good knowledge of the company’s operation, the macro environment that surrounds the company, the industry in which they compete, and customers, among others.
You will also need to be familiar with how companies in the industry deal with the problems you want to solve, and the methods proposed in the peer-review journals.
Criteria for Success is how well you solved the problem(s).
1.7.2 (2) Basic/Applied Academic Research Project (aka, Change-the-World Project)
There is no specific company for whom you will solve business problems, as you are interested in advancing the knowledge in the literature through your project.
The goal is to create new, novel discoveries that will benefit the industry, field, or humankind.
As such, this project will follow a traditional peer-reviewed research format.
Criteria for Success is “Are there significant contributions (i.e., the novelty of your discovery) you make with the project?”
If you are working on a Basic/Applied Academic Research Project and wish to follow a Client Consulting Project approach, you could pretend to be consultants for an existing company. In this case, the client company of your choice should be a publicly traded company to ensure their information are available in major library databases. Talk to your instructor and faculty lead about this possibility.
2 Division of work across courses, faculty lead, and team members
To make the workload reasonable, the project will be done in three semesters. See below for major milestones for the overall project for each semester.
2.1 Fall Semester: IBM 6500 / IBM 6200
Tip
Teams will be formed after project forums and will kick off the project as early as they can. See ?@sec-project-milestones-and-timelines for milestones and timelines.
There are two presentations for students to make, including proposal defense at the end of the semester.
Scope of work will include completing first three chapters out of five chapters. Be familiar with the requirements listed next for Chapter 1 through Chapter 3.
2.1.1 Chapter 1 (Introduction)
Students will learn the content and format of Chapter 1 (Introduction), including basic concepts of problem statements, analytics objectives, and the intended contribution (significance) of the research project.
2.1.2 Chapter 2 (Background and Literature Review)
Students will learn the content and format requirements for Chapter 2 (Background and Literature Review), which includes the company’s background, current marketing plan and digital marketing practices, competitive analysis, customer analysis, SWOT analysis, and literature reviews to gather sufficient information about the problems and initially proposed analytics objectives.
2.1.3 Chapter 3 (Methods)
Students will learn the content and format of Chapter 3 (Methods) and will be provided with guidance on various method topics, including sampling, measures, analytics methods for each analytics objective, and instruments for data collection (if needed).
For Chapter 3, students are also advised to seek feedback from the instructors of IBM 6500 (Customer Analytics Methods and Survey Research).
By the end of this course, groups will present their project proposal. This proposal should demonstrate the current issues or knowledge gaps, analytics objectives, instruments for data collection, and a data collection plan, representing the complete content of Chapter 3.
There will be three different routes to prepare for Ch3, depending on whether or not there is data secured already:
(1) Primary Data: Requiring opinions of human subjects
(2) Secondary Data: Requiring collection (e.g., GA4 data, publicly or privately available) or retrieval from an archive (e.g., Warton Research Data Services, Census data, etc.)
(3) Secured Data: This could be either primary or secondary data that has been obtained already.
2.1.3.1 (1) Primary Data Requiring Opinions of Human Subjects:
For the projects that need opinions of human subjects, students must document in their MSDM CEP Written Report, a survey instrument, a receipt of IRB (Institutional Review Board) application submission, and a timeline of data collection aimed to complete it by the start of Spring semester as part of Chapter 3. The data collection should be completed prior to Week 15 of the fall semester when students make a final proposal presentation to ensure timely completion of the project.
Please keep in mind that data collection takes at least twice as long as your estimated time. This means that the team must apply to the IRB as early as possible during the fall semester and apply for IRB approval by Week 10. Note that IRB does not meet during winter break.
If students are fortunate enough to receive approval to collect data in their first round of review, they should proceed with data collection during the fall semester and should complete data collection during the fall so that they can have the data in hand prior to the start of the spring semester.
Expect to get a revision request from IRB. You must return to them immediately after addressing the concerns to expedite the process.
2.1.3.2 (2) Secondary Data Requiring Collection or Retrieval of Data From an Archive or Websites (e.g., GA4):
Expect a different time needed to secure data if you plan to obtain secondary data. For example, if you plan to obtain data from your client about their internal data, you need to coordinate with the client to ensure they feel comfortable sharing the data with you. They would normally go through an anonymizing process that could take months. Also, secondary data is messy. You need to spend considerable time to make sense out of data, unlike the primary data described above. Thus, Chapter 3 should include the plans and timelines for data cleaning and preparation. Initial data wrangling and visualization must be done prior to the start of the Spring semester. Plus, you may need IRB approval if the data contains any information that may potentially trace individuals and thus threaten their privacy. Thus, be specific about the timeline of the following:
Data source
Information to be included in the data
Justification for the (no) requirement of the IRB application
Data Collection Methods
Retrieval from the client’s CRM by the client company employee
Web scraping by the students
Initial Data Cleaning and Preparation
Timeline of each step of the data procurement process. The timeline should cover the entire process during the fall and winter break and must be done at the start of the Spring semester.
2.1.3.3 (3) Secured Data:
Just because data is already secured for the project does not mean you have nothing to worry about during the fall and winter break. Oftentimes, the data may not be appropriate to address your analytics objectives. The devil is in the details. You need to examine the data to ensure that it contains all the necessary variables for the analytics objectives. If not, you have two options. You can secure better data or change your analytics objectives. Thus, your Chapter 3 should document the due diligence during the fall semester and beyond, leading up to the Start of the Spring semester as follows:
Variables included in the data. Be specific about the type of data (nominal, ordinal, interval, ratio) and the completeness of data.
Distribution of the data. Use a histogram or bar plot to show the central tendency and variance of each variable to be used for your Analytics Objectives.
Identification of the variables and Methods for the AO. Secondary data often employs prediction methods in addition to inferential methods.
Show your research on the methods that could be possible for achieving your AO.
State which methods you plan to use and why.
State the names of the variables in the data with specific labels used in the data
2.1.4Responsibility of Faculty Project Lead
Based on the feedback received from the fall semester, students will address the feedback prior to the Spring semester, which could involve significant and sustained work during winter break. If students or faculty leads are not available to work, they need to factor that in during the fall semester’s workload.
Note that if students cannot collect data during the fall, they will be given incomplete grade in IBM 6500.
If students cannot complete data collection by the fall semester, faculty project leads should provide appropriate instructions and arrangements for students to work during the winter break to get the project ready for the agenda for the Spring semester. Specific activities will differ depending on the type of data collection.
Primary Data: Requiring opinions of human subjects
Secondary Data: Requiring collection (e.g., GA4 data, publicly or privately available) or retrieval of data from an archive (e.g., Warton Research Data Services, Census data, etc.)
Secured Data: This could be either primary or secondary data that has already been obtained.
2.2 Spring Semester: IBM 6800
There will be two presentations.
In the first presentation, teams will present Chapters 1 through 3, plus initial findings of research (mostly descriptive analytics) of Chapter 4, to the faculty and clients, in the first presentation.
In the second presentation, teams will finalize data analysis, incorporating the feedback they received, and present the updates to the faculty and the class. The main goal is to complete their intended analyses appropriate for addressing their AO and provide recommendations based on insights gained from the findings of data analysis.
The teams with consulting clients should arrange a separate meeting with the client to present their research and recommendations (e.g., digital marketing plans) to the client companies. Alternatively, they can invite the client to the presentation.
Once the clients agree with the recommendations, the teams should provide to create specific digital marketing plans to implement for the clients. This may occur during the spring or over the break before summer begins, allowing sufficient time to observe and measure the plan’s effectiveness.
2.3 Summer Term: IBM 6950
There will be two presentations.
In the first presentation, students will present specific digital marketing plans that have been successfully implemented.
If clients permit, teams can implement the plans in collaboration with the clients as early as possible, before the classroom presentation to the faculty and the students.
Implementing plans early gives you more time to collect metrics and present your findings.
In the second presentation, the students will present their implementation efforts and outcomes.
Compare metrics before and after the implementation of the team’s recommendations.
Share lessons you learned.
Lay out directions for future marketing and analytics efforts.
3 Student Presentations
3.1 (1) Initial Proposal Presentation (Week 09)
The initial proposal will consist of three (introduction, background, and methods) of the five chapters, and should contain the necessary information (a sample will be provided) about the client and its business, macro-environmental factors, and relevant background information for the project objectives (e.g., consumer behavior theory, alternative models/frameworks for the topic) that will give students sufficient background to understand the context and assist generation of meaningful recommendations for the client company or insights for the for the field/humankind. In addition, the proposal should include a plan to deliver one or more of the following outputs (e.g., strategies, recommendations, implementation of specific digital marketing plans, insights as deliverables at the conclusion of the project. For example:
New or revised (digital) marketing strategies and specific plans
New statistical insights previously unavailable to the company, industry, or the profession.
New metrics and key performance indicators (KPIs).
Process recommendations and models.
Predictive analytics approaches.
Data management recommendations.
People and process recommendations to support your data science practice.
3.2 (2) Final Proposal Defense Presentation (Week 15)
In this final proposal presentation, you should adress all the concerns raised by the faculty. Student teams will need to demonstrate that (1) they have appropriate data for the said analytics objectives, (2) they have a solid analytics plan for the analytics objectives, and (3) they have prepared data (data wrangling) for modeling and confirm that the data is adequate for addressing the said AOs.
If any deficiencies are identified in the proposal defense, students should address the concerns raised by the professors and submit a revised proposal before the spring semester. The teams should address any concerns during the winter before proceeding to the next stage in the spring.
3.3 (3) MSDM Project Initial Data Analysis Presentation – Week 6, Spring
In this presentation, students should demonstrate that they have addressed all the deficiencies identified in their initial proposal presentation and addressed them to the full satisfaction of their faculty project lead.
In addition, student teams will need to demonstrate that (1) they have prepared data (data wrangling) for modeling, and (4) they analyzed and generated insights from the data according to the planned methods for the analytics objectives. Descriptive analytics should be used as an exploratory analysis for the said analytics objectives with an emphasis on visualization. Predictive analytics are not required for this presentation; however, teams may proceed if they wish to exceed expectations.
3.4 (4) Data Analysis Finalization and Recommendation Presentation – Week 14, Spring
In this presentation, the teams should address any concerns raised in their previous presentations.
Furthermore, students should complete all the remaining analytical procedures outlined in Chapter 3 (Methods), generate insights, make recommendations based on the research findings, and present their work to the faculty and fellow students. The scope of the work includes Chapters 4 and the earlier part of Chapter 5.
Immediately after making recommendations to the client company, students should follow up to obtain approval of their recommendations and begin working on the specifics of digital marketing plans (e.g., social media posts, video creation), planning to implement the recommended course of action. This will require working closely with client firm managers. You need to implement the plans (e.g., social media content, SEO, paid ads, etc.) prior to Week 4 of Summer.
By the presentation time, students should have implemented the data-informed marketing plans.
In this presentation, students will share what they have done since the last presentation, focusing on how their recommendations led to the final form of implemented marketing strategies and tactics.
In this final presentation, students will reflect on the impact of data-informed recommendations by comparing the results before and after the implementation of marketing strategies and tactics, using relevant key performance indicators. Students will showcase the entire project, with a focus on performance metrics, lessons learned, and directions for future efforts.
4 Major Deliverables
4.1 Written Reports:
Tip
While your instructors will provide you with mini assignments and deliverables for step-by-step project development in the various courses, the following documents must be submitted as outlined below.
Do not make a separate document for each submission. Use the same document all the time by submitting the URL of the shareable written report.
The easiest way to start documenting your project is to download the content and format guidelines file and fill out the template as you go.
Do not worry about filling out front end sections prior to Chapter 1. Front-end sections will easier to fill out after all five chapters are done during the summer, the last semester of your journey.
Below is the sections of the written report you will be working in sequence.
Initial Project Proposal (Ch1 - Ch3): By Week 9 of the fall semester (collected by IBM 6500 instructor)
Final Project Proposal (Ch1 - Ch3): By Week 15 of the fall semester (collected by IBM 6500 instructor)
Initial Data Analysis (Ch4): By Week 7 of Spring in IBM 6800
Data Analysis Finalization and Recommendations (Ch4 and Ch5): by Week 14 of IBM 6800
Marketing Plan Implementation and Update (Ch5): by Week 5 of Summer in IBM 6950
Post Implementation Reflection and Conclusion(Ch5): By Week 9 of Summer in IBM 6950
Title Page, Abstract, and Individual Contribution Statement (Template): by the end of Week 10 in IBM 6950 to the Graduate Studies Committee and CPP Library (this will be searchable and accessible from around the world).
Complete Final Project Report: by the end of Week 11 of summer in IBM 6950 (will be kept in the office of the MSDM Program)
4.2 Presentations:
Each semester, instructors require groups to present in class for progress checks and feedback regularly. Additionally, there will be three major presentations to which all MSDM faculty, clients, and students are invited to attend as guests.
Initial Proposal Presentation: In the fall: IBM 6200 & IBM 6500
Coverage:
Introduction (Ch1)
Background and Analytics Objectives (Ch2)
Proposed Method (Ch3): For primary data collection, questionnaire should be presented.
Final Proposal Defense Presentation In the fall: IBM 6200 & IBM 6500
Coverage:
Introduction (Ch1)
Background and Analytics Objectives (Ch2)
Methods (Ch3): At least sample characteristics and variables to be used for each AO should be presented.
Initial Data Analysis Presentation: in the spring: IBM 6800 and IBM 6400
Coverage:
Descriptive Analytics (Ch4)*
Predictive Analytics (Ch4)
Note: Should be the focus.
Data Analysis Finalization and Recommendation Presentation: in the Spring: IBM 6800 and IBM 6400
Coverage:
Predictive Analytics (Ch4)
Summary of findings (Ch5)
Recommendations (Ch5)
Data-Informed Marketing Plan Implementation Update Presentation: In the summer: IBM 6950
Coverage:
Marketing Plan and Implementation (Ch5)*
Note: *Once the recommendations are approved, students and clients can collaborate to determine which recommendations will be incorporated into the marketing plan and implemented, working closely with the client. This presentation is to share your progress.
Post-Implementation Reflection Presentation: In the summer: IBM 6950
Coverage:
Post Implementation Reflection (Ch5)*
Conclusion (limitations and Directions for future efforts) (Ch5)
Note: *The focus will be on how performance metrics are after the data-informed recommendations are implemented, compared to the performance before the implementation.
5 Content and Format Template
Use this template for your project by removing the first page and starting to fill out the blanks on the second page.
Download the file below and use it as a template for your written report, which will be the only report you will need to submit across three semesters. Each time you are asked to submit your report, you will submit the same link to the report.
(Download the file and use it as a template for your written report.)
Put your written report and any other files, for that matter, into a cloud storage such as Google Docs to share your progress with your teammates, faculty lead, and class instructors.